Abstract. Modern satellite networks with rapid image acquisition cycles allow for near-real-time imaging of areas impacted by natural hazards such as mass wasting, flooding, and volcanic eruptions. Publicly accessible multi-spectral datasets (e.g., Landsat, Sentinel-2) are particularly helpful in analyzing the spatial extent of disturbances, however, the datasets are large and require intensive processing on high-powered computers by trained analysts. HazMapper is an open-access hazard mapping application developed in Google Earth Engine that allows users to derive map and GIS-based products from Sentinel or Landsat datasets without the time- and cost-intensive resources required for traditional analysis. The first iteration of HazMapper relies on a vegetation-based metric, the relative difference in the normalized difference vegetation index (rdNDVI), to identify areas on the landscape where vegetation was removed following a natural disaster. Because of the vegetation-based metric, the tool is typically not suitable for use in desert or polar regions. HazMapper is not a semi-automated routine but makes rapid and repeatable analysis and visualization feasible for both recent and historical natural disasters. Case studies are included for the identification of landslides and debris flows, wildfires, pyroclastic flows, and lava flow inundation. HazMapper is intended for use by both scientists and non-scientists, such as emergency managers and public safety decision-makers.

an active-source seismic survey was performed over the Eastern Lau Spreading Center in the Lau Back-Arc Basin (21 S, 176 S). Acoustic signals generated by the R/V Langseth's 36-gun pneumatic source array were recorded within the deep sound channel at offsets of 29-416 km. The local ocean acoustic environment is everywhere bottom limited, with seafloor depths within the study domain ranging from $1700-2800 m. Low-frequency (4-125 Hz) sound levels are monitored using root-mean-square, energy-flux-density and zero-to-peak measurement techniques. From these field data, transmission loss is found to exceed the predictions of a geometric spherical spreading model. At similar ranges, arrival amplitudes vary by up to 20 dB and durations vary by a factor of three to six. The depth of the seafloor beneath the air gun source exhibits a positive correlation with arrival duration and a negative correlation with range-corrected amplitude, explaining up to 30% of the observed variation in both parameters. The strength of this correlation, however, varies for stations lying at different azimuths, highlighting the importance of seafloor aspect and slope in the coupling of bottom-interacting acoustic energy into the sound channel. Range-dependent ray tracing shows that shots deployed over shallower seafloor are more likely to produce sound channel trapped signals that propagate with limited bottom interaction. This results in arrivals that are more impulsive, with shorter durations and higher amplitudes. Shots deployed in deeper water typically undergo a larger number of bounces and are characterized by more emergent, longer duration and smaller amplitude arrivals.

On August 14, 2021, a Mw 7.2 earthquake struck the Tiburon Peninsula of western Haiti triggering thousands of landslides. Three days after the earthquake on August 17, 2021, Tropical Storm Grace crossed shallow waters offshore of southern Haiti triggering more landslides worsening the situation. In the aftermath of these events, several organizations with disaster response capabilities or programs activated to provide information on the location of landslides to first responders on the ground. Utilizing remote sensing to support rapid response, one organization manually mapped initiation point of landslides and three automatically detected landslides. The 2021 Haiti event also provided a unique opportunity to test different automated landslide detection methods that utilized both SAR and optical data in a rapid response scenario where rapid situational awareness was critical. As the methods used are highly replicable, the main goal of this study is to summarize the landslide rapid response products released by the organizations, detection methods, quantify accuracy and provide guidelines on how some of the shortcomings encountered in this effort might be addressed in the future. To support this validation, a manually mapped polygon-based landslide inventory covering the entire affected area was created and is also released through this effort.